Overview

Dataset statistics

Number of variables30
Number of observations10001
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory232.0 B

Variable types

Numeric19
Categorical11

Warnings

Age is highly correlated with IncomeHigh correlation
Income is highly correlated with AgeHigh correlation
Freq is highly correlated with Monetary and 1 other fieldsHigh correlation
Monetary is highly correlated with Freq and 1 other fieldsHigh correlation
LTV is highly correlated with Freq and 1 other fieldsHigh correlation
SMRack is highly correlated with LGRack and 7 other fieldsHigh correlation
LGRack is highly correlated with SMRack and 7 other fieldsHigh correlation
Humid is highly correlated with SMRack and 7 other fieldsHigh correlation
Spcork is highly correlated with SMRack and 7 other fieldsHigh correlation
Bucket is highly correlated with SMRack and 7 other fieldsHigh correlation
Access is highly correlated with SMRack and 7 other fieldsHigh correlation
Complain is highly correlated with SMRack and 7 other fieldsHigh correlation
Mailfriend is highly correlated with SMRack and 7 other fieldsHigh correlation
Emailfriend is highly correlated with SMRack and 7 other fieldsHigh correlation
Custid is uniformly distributed Uniform
LTV has 269 (2.7%) zeros Zeros
Perdeal has 144 (1.4%) zeros Zeros
Sweetred has 921 (9.2%) zeros Zeros
Sweetwh has 909 (9.1%) zeros Zeros
Dessert has 927 (9.3%) zeros Zeros
Exotic has 104 (1.0%) zeros Zeros

Reproduction

Analysis started2021-02-23 12:06:48.506719
Analysis finished2021-02-23 12:07:41.655220
Duration53.15 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Custid
Real number (ℝ≥0)

UNIFORM

Distinct10000
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6000.5
Minimum1001
Maximum11000
Zeros0
Zeros (%)0.0%
Memory size78.3 KiB
2021-02-23T12:07:41.747999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1500.95
Q13500.75
median6000.5
Q38500.25
95-th percentile10500.05
Maximum11000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.89568
Coefficient of variation (CV)0.4811091876
Kurtosis-1.2
Mean6000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum60005000
Variance8334166.667
MonotocityNot monotonic
2021-02-23T12:07:41.892968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97351
 
< 0.1%
85331
 
< 0.1%
77881
 
< 0.1%
38431
 
< 0.1%
52841
 
< 0.1%
47431
 
< 0.1%
12471
 
< 0.1%
34091
 
< 0.1%
11971
 
< 0.1%
104791
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
10011
< 0.1%
10021
< 0.1%
10031
< 0.1%
10041
< 0.1%
10051
< 0.1%
10061
< 0.1%
10071
< 0.1%
10081
< 0.1%
10091
< 0.1%
10101
< 0.1%
ValueCountFrequency (%)
110001
< 0.1%
109991
< 0.1%
109981
< 0.1%
109971
< 0.1%
109961
< 0.1%
109951
< 0.1%
109941
< 0.1%
109931
< 0.1%
109921
< 0.1%
109911
< 0.1%

Dayswus
Real number (ℝ≥0)

Distinct702
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean898.102
Minimum550
Maximum1250
Zeros0
Zeros (%)0.0%
Memory size78.3 KiB
2021-02-23T12:07:42.048208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum550
5-th percentile586
Q1724
median894
Q31074
95-th percentile1216
Maximum1250
Range700
Interquartile range (IQR)350

Descriptive statistics

Standard deviation202.4826639
Coefficient of variation (CV)0.2254561998
Kurtosis-1.200677417
Mean898.102
Median Absolute Deviation (MAD)176
Skewness0.02359517137
Sum8981918.102
Variance40999.2292
MonotocityNot monotonic
2021-02-23T12:07:42.175955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119929
 
0.3%
95327
 
0.3%
92124
 
0.2%
67124
 
0.2%
58724
 
0.2%
107924
 
0.2%
111523
 
0.2%
63523
 
0.2%
79423
 
0.2%
89223
 
0.2%
Other values (692)9757
97.6%
ValueCountFrequency (%)
55014
0.1%
5519
0.1%
55212
0.1%
55316
0.2%
55415
0.1%
55516
0.2%
55615
0.1%
55717
0.2%
55815
0.1%
5599
0.1%
ValueCountFrequency (%)
12502
 
< 0.1%
124912
0.1%
124813
0.1%
124720
0.2%
124612
0.1%
124517
0.2%
124415
0.1%
124315
0.1%
124214
0.1%
124111
0.1%

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.9273
Minimum18
Maximum78
Zeros0
Zeros (%)0.0%
Memory size78.3 KiB
2021-02-23T12:07:42.330956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q133
median48
Q363
95-th percentile75
Maximum78
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.30185582
Coefficient of variation (CV)0.3610020973
Kurtosis-1.19692013
Mean47.9273
Median Absolute Deviation (MAD)15
Skewness-0.01035634623
Sum479320.9273
Variance299.3542147
MonotocityNot monotonic
2021-02-23T12:07:42.469478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63190
 
1.9%
22188
 
1.9%
65188
 
1.9%
53187
 
1.9%
35186
 
1.9%
61183
 
1.8%
55183
 
1.8%
40182
 
1.8%
38182
 
1.8%
54180
 
1.8%
Other values (52)8152
81.5%
ValueCountFrequency (%)
1890
0.9%
19169
1.7%
20175
1.7%
21176
1.8%
22188
1.9%
23157
1.6%
24149
1.5%
25164
1.6%
26167
1.7%
27162
1.6%
ValueCountFrequency (%)
7881
0.8%
77160
1.6%
76156
1.6%
75150
1.5%
74171
1.7%
73158
1.6%
72173
1.7%
71163
1.6%
70163
1.6%
69155
1.5%

Edu
Real number (ℝ≥0)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.7391
Minimum12
Maximum20
Zeros0
Zeros (%)0.0%
Memory size78.3 KiB
2021-02-23T12:07:42.599252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile14
Q115
median17
Q318
95-th percentile20
Maximum20
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.876281213
Coefficient of variation (CV)0.1120897308
Kurtosis-0.8286058243
Mean16.7391
Median Absolute Deviation (MAD)2
Skewness-0.06576309637
Sum167407.7391
Variance3.52043119
MonotocityNot monotonic
2021-02-23T12:07:42.704715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
161709
17.1%
151708
17.1%
171603
16.0%
181598
16.0%
191463
14.6%
14977
9.8%
20683
 
6.8%
13169
 
1.7%
1290
 
0.9%
16.73911
 
< 0.1%
ValueCountFrequency (%)
1290
 
0.9%
13169
 
1.7%
14977
9.8%
151708
17.1%
161709
17.1%
16.73911
 
< 0.1%
171603
16.0%
181598
16.0%
191463
14.6%
20683
 
6.8%
ValueCountFrequency (%)
20683
 
6.8%
191463
14.6%
181598
16.0%
171603
16.0%
16.73911
 
< 0.1%
161709
17.1%
151708
17.1%
14977
9.8%
13169
 
1.7%
1290
 
0.9%

Income
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9459
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69904.358
Minimum10000
Maximum140628
Zeros0
Zeros (%)0.0%
Memory size78.3 KiB
2021-02-23T12:07:42.842774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile26152
Q147646
median70009
Q392147
95-th percentile113311
Maximum140628
Range130628
Interquartile range (IQR)44501

Descriptive statistics

Standard deviation27610.85266
Coefficient of variation (CV)0.3949804197
Kurtosis-0.9246030945
Mean69904.358
Median Absolute Deviation (MAD)22263
Skewness0.005863360532
Sum699113484.4
Variance762359184.9
MonotocityNot monotonic
2021-02-23T12:07:42.983481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000040
 
0.4%
948174
 
< 0.1%
641854
 
< 0.1%
517433
 
< 0.1%
591453
 
< 0.1%
994523
 
< 0.1%
664413
 
< 0.1%
761333
 
< 0.1%
384933
 
< 0.1%
1069953
 
< 0.1%
Other values (9449)9932
99.3%
ValueCountFrequency (%)
1000040
0.4%
101821
 
< 0.1%
101861
 
< 0.1%
106081
 
< 0.1%
108861
 
< 0.1%
113471
 
< 0.1%
113601
 
< 0.1%
114371
 
< 0.1%
114741
 
< 0.1%
116831
 
< 0.1%
ValueCountFrequency (%)
1406281
< 0.1%
1397301
< 0.1%
1373381
< 0.1%
1370531
< 0.1%
1369221
< 0.1%
1366901
< 0.1%
1362131
< 0.1%
1361921
< 0.1%
1359591
< 0.1%
1357891
< 0.1%

Kidhome
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size586.1 KiB
0.0
5812 
1.0
4188 
0.4188
 
1

Length

Max length6
Median length3
Mean length3.00029997
Min length3

Characters and Unicode

Total characters30006
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
0.05812
58.1%
1.04188
41.9%
0.41881
 
< 0.1%
2021-02-23T12:07:43.261441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T12:07:43.346432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.05812
58.1%
1.04188
41.9%
0.41881
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
015813
52.7%
.10001
33.3%
14189
 
14.0%
82
 
< 0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20005
66.7%
Other Punctuation10001
33.3%

Most frequent character per category

ValueCountFrequency (%)
015813
79.0%
14189
 
20.9%
82
 
< 0.1%
41
 
< 0.1%
ValueCountFrequency (%)
.10001
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30006
100.0%

Most frequent character per script

ValueCountFrequency (%)
015813
52.7%
.10001
33.3%
14189
 
14.0%
82
 
< 0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30006
100.0%

Most frequent character per block

ValueCountFrequency (%)
015813
52.7%
.10001
33.3%
14189
 
14.0%
82
 
< 0.1%
41
 
< 0.1%

Teenhome
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size586.1 KiB
0.0
5302 
1.0
4698 
0.4698
 
1

Length

Max length6
Median length3
Mean length3.00029997
Min length3

Characters and Unicode

Total characters30006
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
0.05302
53.0%
1.04698
47.0%
0.46981
 
< 0.1%
2021-02-23T12:07:43.600813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T12:07:43.690130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.05302
53.0%
1.04698
47.0%
0.46981
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
015303
51.0%
.10001
33.3%
14698
 
15.7%
41
 
< 0.1%
61
 
< 0.1%
91
 
< 0.1%
81
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20005
66.7%
Other Punctuation10001
33.3%

Most frequent character per category

ValueCountFrequency (%)
015303
76.5%
14698
 
23.5%
41
 
< 0.1%
61
 
< 0.1%
91
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
.10001
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30006
100.0%

Most frequent character per script

ValueCountFrequency (%)
015303
51.0%
.10001
33.3%
14698
 
15.7%
41
 
< 0.1%
61
 
< 0.1%
91
 
< 0.1%
81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30006
100.0%

Most frequent character per block

ValueCountFrequency (%)
015303
51.0%
.10001
33.3%
14698
 
15.7%
41
 
< 0.1%
61
 
< 0.1%
91
 
< 0.1%
81
 
< 0.1%

Freq
Real number (ℝ≥0)

HIGH CORRELATION

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.6280372
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Memory size39.2 KiB
2021-02-23T12:07:43.795204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median12
Q324
95-th percentile37
Maximum56
Range55
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.9684765
Coefficient of variation (CV)0.8181874536
Kurtosis-0.5911677056
Mean14.6280372
Median Absolute Deviation (MAD)9
Skewness0.6951613237
Sum146295
Variance143.2444298
MonotocityNot monotonic
2021-02-23T12:07:43.909952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3857
 
8.6%
2836
 
8.4%
4789
 
7.9%
5557
 
5.6%
1541
 
5.4%
7250
 
2.5%
6239
 
2.4%
10235
 
2.3%
12233
 
2.3%
9230
 
2.3%
Other values (44)5234
52.3%
ValueCountFrequency (%)
1541
5.4%
2836
8.4%
3857
8.6%
4789
7.9%
5557
5.6%
6239
 
2.4%
7250
 
2.5%
8230
 
2.3%
9230
 
2.3%
10235
 
2.3%
ValueCountFrequency (%)
561
 
< 0.1%
542
 
< 0.1%
531
 
< 0.1%
522
 
< 0.1%
507
 
0.1%
4910
0.1%
488
 
0.1%
4716
0.2%
4619
0.2%
4523
0.2%

Recency
Real number (ℝ≥0)

Distinct390
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.4068
Minimum0
Maximum549
Zeros46
Zeros (%)0.5%
Memory size78.3 KiB
2021-02-23T12:07:44.031472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q126
median52
Q378
95-th percentile99
Maximum549
Range549
Interquartile range (IQR)52

Descriptive statistics

Standard deviation69.87076151
Coefficient of variation (CV)1.119601734
Kurtosis20.95810519
Mean62.4068
Median Absolute Deviation (MAD)26
Skewness4.165625545
Sum624130.4068
Variance4881.923314
MonotocityNot monotonic
2021-02-23T12:07:44.135904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92114
 
1.1%
64113
 
1.1%
56113
 
1.1%
9113
 
1.1%
4113
 
1.1%
27111
 
1.1%
29111
 
1.1%
24110
 
1.1%
45109
 
1.1%
13108
 
1.1%
Other values (380)8886
88.9%
ValueCountFrequency (%)
046
0.5%
1103
1.0%
2103
1.0%
3100
1.0%
4113
1.1%
579
0.8%
6102
1.0%
794
0.9%
886
0.9%
9113
1.1%
ValueCountFrequency (%)
5493
< 0.1%
5471
 
< 0.1%
5463
< 0.1%
5431
 
< 0.1%
5422
< 0.1%
5401
 
< 0.1%
5381
 
< 0.1%
5371
 
< 0.1%
5351
 
< 0.1%
5341
 
< 0.1%

Monetary
Real number (ℝ≥0)

HIGH CORRELATION

Distinct723
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean622.5552
Minimum6
Maximum3052
Zeros0
Zeros (%)0.0%
Memory size78.3 KiB
2021-02-23T12:07:44.322833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q163
median383
Q31077
95-th percentile1916
Maximum3052
Range3046
Interquartile range (IQR)1014

Descriptive statistics

Standard deviation647.1029658
Coefficient of variation (CV)1.039430665
Kurtosis-0.07157312418
Mean622.5552
Median Absolute Deviation (MAD)344
Skewness0.9760857124
Sum6226174.555
Variance418742.2484
MonotocityNot monotonic
2021-02-23T12:07:44.440978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19171
 
1.7%
41140
 
1.4%
20120
 
1.2%
6499
 
1.0%
4298
 
1.0%
4097
 
1.0%
6584
 
0.8%
6682
 
0.8%
9264
 
0.6%
3363
 
0.6%
Other values (713)8983
89.8%
ValueCountFrequency (%)
61
 
< 0.1%
72
 
< 0.1%
89
 
0.1%
915
 
0.1%
1025
0.2%
1129
0.3%
1226
0.3%
1334
0.3%
1441
0.4%
1529
0.3%
ValueCountFrequency (%)
30521
< 0.1%
29381
< 0.1%
29361
< 0.1%
28781
< 0.1%
28231
< 0.1%
28211
< 0.1%
27071
< 0.1%
27061
< 0.1%
27052
< 0.1%
27042
< 0.1%

LTV
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1209
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.0712
Minimum-178
Maximum1791
Zeros269
Zeros (%)2.7%
Memory size78.3 KiB
2021-02-23T12:07:44.596206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-178
5-th percentile-19
Q1-2
median57
Q3364
95-th percentile828
Maximum1791
Range1969
Interquartile range (IQR)366

Descriptive statistics

Standard deviation291.9714406
Coefficient of variation (CV)1.396516788
Kurtosis1.540915322
Mean209.0712
Median Absolute Deviation (MAD)69
Skewness1.446813413
Sum2090921.071
Variance85247.32213
MonotocityNot monotonic
2021-02-23T12:07:44.764960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2323
 
3.2%
0269
 
2.7%
-3253
 
2.5%
-4241
 
2.4%
-1241
 
2.4%
-5218
 
2.2%
2186
 
1.9%
1182
 
1.8%
-6162
 
1.6%
-7148
 
1.5%
Other values (1199)7778
77.8%
ValueCountFrequency (%)
-1781
 
< 0.1%
-1681
 
< 0.1%
-1601
 
< 0.1%
-1472
< 0.1%
-1381
 
< 0.1%
-1371
 
< 0.1%
-1341
 
< 0.1%
-1243
< 0.1%
-1231
 
< 0.1%
-1172
< 0.1%
ValueCountFrequency (%)
17911
< 0.1%
16081
< 0.1%
15881
< 0.1%
15831
< 0.1%
15491
< 0.1%
15151
< 0.1%
15131
< 0.1%
15101
< 0.1%
14961
< 0.1%
14781
< 0.1%

Perdeal
Real number (ℝ≥0)

ZEROS

Distinct99
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.3972
Minimum0
Maximum97
Zeros144
Zeros (%)1.4%
Memory size78.3 KiB
2021-02-23T12:07:44.934881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median25
Q356
95-th percentile82
Maximum97
Range97
Interquartile range (IQR)50

Descriptive statistics

Standard deviation27.89569917
Coefficient of variation (CV)0.8610527814
Kurtosis-1.054017183
Mean32.3972
Median Absolute Deviation (MAD)22
Skewness0.5192531345
Sum324004.3972
Variance778.1700322
MonotocityNot monotonic
2021-02-23T12:07:45.068990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1885
 
8.8%
2624
 
6.2%
3359
 
3.6%
4232
 
2.3%
5195
 
1.9%
7175
 
1.7%
6164
 
1.6%
10151
 
1.5%
9147
 
1.5%
12146
 
1.5%
Other values (89)6923
69.2%
ValueCountFrequency (%)
0144
 
1.4%
1885
8.8%
2624
6.2%
3359
3.6%
4232
 
2.3%
5195
 
1.9%
6164
 
1.6%
7175
 
1.7%
8139
 
1.4%
9147
 
1.5%
ValueCountFrequency (%)
971
 
< 0.1%
969
 
0.1%
953
 
< 0.1%
949
 
0.1%
9331
0.3%
9229
0.3%
9120
0.2%
9032
0.3%
8948
0.5%
8846
0.5%

Dryred
Real number (ℝ≥0)

Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.3827
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Memory size78.3 KiB
2021-02-23T12:07:45.201554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q132
median51
Q369
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)37

Descriptive statistics

Standard deviation23.45264251
Coefficient of variation (CV)0.4654899898
Kurtosis-0.9261405286
Mean50.3827
Median Absolute Deviation (MAD)18
Skewness-0.07844917351
Sum503877.3827
Variance550.0264407
MonotocityNot monotonic
2021-02-23T12:07:45.336048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40158
 
1.6%
55158
 
1.6%
47153
 
1.5%
70153
 
1.5%
56152
 
1.5%
41151
 
1.5%
46151
 
1.5%
57149
 
1.5%
58149
 
1.5%
48148
 
1.5%
Other values (90)8479
84.8%
ValueCountFrequency (%)
12
 
< 0.1%
215
 
0.1%
327
 
0.3%
444
0.4%
551
0.5%
664
0.6%
763
0.6%
865
0.6%
976
0.8%
1068
0.7%
ValueCountFrequency (%)
991
 
< 0.1%
981
 
< 0.1%
979
 
0.1%
9619
 
0.2%
9525
 
0.2%
9441
0.4%
9359
0.6%
9255
0.5%
9166
0.7%
9069
0.7%

Sweetred
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0545
Minimum0
Maximum75
Zeros921
Zeros (%)9.2%
Memory size78.3 KiB
2021-02-23T12:07:45.475688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310
95-th percentile23
Maximum75
Range75
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.866150885
Coefficient of variation (CV)1.115054346
Kurtosis5.771802544
Mean7.0545
Median Absolute Deviation (MAD)3
Skewness2.061381057
Sum70552.0545
Variance61.87632975
MonotocityNot monotonic
2021-02-23T12:07:45.588632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11458
14.6%
21131
11.3%
0921
 
9.2%
3867
 
8.7%
4708
 
7.1%
5645
 
6.4%
6550
 
5.5%
7447
 
4.5%
8411
 
4.1%
9317
 
3.2%
Other values (50)2546
25.5%
ValueCountFrequency (%)
0921
9.2%
11458
14.6%
21131
11.3%
3867
8.7%
4708
7.1%
5645
6.4%
6550
 
5.5%
7447
 
4.5%
7.05451
 
< 0.1%
8411
 
4.1%
ValueCountFrequency (%)
751
< 0.1%
681
< 0.1%
671
< 0.1%
651
< 0.1%
611
< 0.1%
591
< 0.1%
582
< 0.1%
551
< 0.1%
511
< 0.1%
501
< 0.1%

Drywh
Real number (ℝ≥0)

Distinct74
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.5213
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Memory size78.3 KiB
2021-02-23T12:07:45.714214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q119
median28
Q337
95-th percentile50
Maximum74
Range73
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.58332811
Coefficient of variation (CV)0.4411905527
Kurtosis-0.4266219477
Mean28.5213
Median Absolute Deviation (MAD)9
Skewness0.3207923803
Sum285241.5213
Variance158.3401463
MonotocityNot monotonic
2021-02-23T12:07:46.295861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23320
 
3.2%
22314
 
3.1%
26308
 
3.1%
27297
 
3.0%
25293
 
2.9%
19287
 
2.9%
30282
 
2.8%
31281
 
2.8%
28278
 
2.8%
29274
 
2.7%
Other values (64)7067
70.7%
ValueCountFrequency (%)
11
 
< 0.1%
22
 
< 0.1%
314
 
0.1%
440
 
0.4%
559
0.6%
669
0.7%
792
0.9%
8108
1.1%
9145
1.4%
10142
1.4%
ValueCountFrequency (%)
742
 
< 0.1%
731
 
< 0.1%
721
 
< 0.1%
701
 
< 0.1%
693
 
< 0.1%
681
 
< 0.1%
671
 
< 0.1%
667
0.1%
655
< 0.1%
6410
0.1%

Sweetwh
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0698
Minimum0
Maximum62
Zeros909
Zeros (%)9.1%
Memory size78.3 KiB
2021-02-23T12:07:46.428061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310
95-th percentile24
Maximum62
Range62
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.014682025
Coefficient of variation (CV)1.13365046
Kurtosis5.664414964
Mean7.0698
Median Absolute Deviation (MAD)3
Skewness2.106884048
Sum70705.0698
Variance64.23512796
MonotocityNot monotonic
2021-02-23T12:07:46.540252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11541
15.4%
21088
10.9%
0909
 
9.1%
3865
 
8.6%
4746
 
7.5%
5605
 
6.0%
6550
 
5.5%
7451
 
4.5%
8376
 
3.8%
9327
 
3.3%
Other values (50)2543
25.4%
ValueCountFrequency (%)
0909
9.1%
11541
15.4%
21088
10.9%
3865
8.6%
4746
7.5%
5605
 
6.0%
6550
 
5.5%
7451
 
4.5%
7.06981
 
< 0.1%
8376
 
3.8%
ValueCountFrequency (%)
621
 
< 0.1%
611
 
< 0.1%
602
< 0.1%
591
 
< 0.1%
571
 
< 0.1%
562
< 0.1%
542
< 0.1%
523
< 0.1%
504
< 0.1%
491
 
< 0.1%

Dessert
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9474
Minimum0
Maximum77
Zeros927
Zeros (%)9.3%
Memory size78.3 KiB
2021-02-23T12:07:46.667340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q39
95-th percentile23
Maximum77
Range77
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.87915181
Coefficient of variation (CV)1.134115181
Kurtosis6.670907124
Mean6.9474
Median Absolute Deviation (MAD)3
Skewness2.203649513
Sum69480.9474
Variance62.08103324
MonotocityNot monotonic
2021-02-23T12:07:46.803721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11495
14.9%
21110
11.1%
0927
 
9.3%
3917
 
9.2%
4741
 
7.4%
5577
 
5.8%
6524
 
5.2%
7496
 
5.0%
8389
 
3.9%
9345
 
3.4%
Other values (50)2480
24.8%
ValueCountFrequency (%)
0927
9.3%
11495
14.9%
21110
11.1%
3917
9.2%
4741
7.4%
5577
 
5.8%
6524
 
5.2%
6.94741
 
< 0.1%
7496
 
5.0%
8389
 
3.9%
ValueCountFrequency (%)
771
 
< 0.1%
721
 
< 0.1%
621
 
< 0.1%
591
 
< 0.1%
582
< 0.1%
572
< 0.1%
561
 
< 0.1%
553
< 0.1%
522
< 0.1%
502
< 0.1%

Exotic
Real number (ℝ≥0)

ZEROS

Distinct98
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.5466
Minimum0
Maximum96
Zeros104
Zeros (%)1.0%
Memory size78.3 KiB
2021-02-23T12:07:46.936522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median10
Q323
95-th percentile55
Maximum96
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.24680922
Coefficient of variation (CV)1.042317408
Kurtosis2.838649566
Mean16.5466
Median Absolute Deviation (MAD)7
Skewness1.71371308
Sum165482.5466
Variance297.4524284
MonotocityNot monotonic
2021-02-23T12:07:47.060759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2700
 
7.0%
1615
 
6.1%
3614
 
6.1%
4573
 
5.7%
5563
 
5.6%
6467
 
4.7%
7402
 
4.0%
8377
 
3.8%
10340
 
3.4%
9332
 
3.3%
Other values (88)5018
50.2%
ValueCountFrequency (%)
0104
 
1.0%
1615
6.1%
2700
7.0%
3614
6.1%
4573
5.7%
5563
5.6%
6467
4.7%
7402
4.0%
8377
3.8%
9332
3.3%
ValueCountFrequency (%)
961
 
< 0.1%
951
 
< 0.1%
943
< 0.1%
932
 
< 0.1%
922
 
< 0.1%
914
< 0.1%
903
< 0.1%
895
< 0.1%
887
0.1%
872
 
< 0.1%

WebPurchase
Real number (ℝ≥0)

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.3762
Minimum4
Maximum88
Zeros0
Zeros (%)0.0%
Memory size78.3 KiB
2021-02-23T12:07:47.187616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q128
median45
Q357
95-th percentile69
Maximum88
Range84
Interquartile range (IQR)29

Descriptive statistics

Standard deviation18.52113586
Coefficient of variation (CV)0.4370645754
Kurtosis-1.037824579
Mean42.3762
Median Absolute Deviation (MAD)14
Skewness-0.2635958551
Sum423804.3762
Variance343.0324736
MonotocityNot monotonic
2021-02-23T12:07:47.299536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56238
 
2.4%
54238
 
2.4%
57235
 
2.3%
55227
 
2.3%
58217
 
2.2%
53216
 
2.2%
13215
 
2.1%
61214
 
2.1%
60209
 
2.1%
59204
 
2.0%
Other values (73)7788
77.9%
ValueCountFrequency (%)
41
 
< 0.1%
54
 
< 0.1%
615
 
0.1%
739
 
0.4%
857
 
0.6%
9103
1.0%
10142
1.4%
11167
1.7%
12184
1.8%
13215
2.1%
ValueCountFrequency (%)
881
 
< 0.1%
841
 
< 0.1%
831
 
< 0.1%
824
 
< 0.1%
813
 
< 0.1%
806
 
0.1%
798
 
0.1%
7812
0.1%
7714
0.1%
7626
0.3%

WebVisit
Real number (ℝ≥0)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.216578342
Minimum0
Maximum10
Zeros32
Zeros (%)0.3%
Memory size39.2 KiB
2021-02-23T12:07:47.402158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.33034099
Coefficient of variation (CV)0.4467182964
Kurtosis-0.9934810938
Mean5.216578342
Median Absolute Deviation (MAD)2
Skewness-0.2786309182
Sum52171
Variance5.430489131
MonotocityNot monotonic
2021-02-23T12:07:47.515973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
71651
16.5%
61467
14.7%
81398
14.0%
51282
12.8%
41045
10.4%
3946
9.5%
2919
9.2%
1749
7.5%
9507
 
5.1%
032
 
0.3%
ValueCountFrequency (%)
032
 
0.3%
1749
7.5%
2919
9.2%
3946
9.5%
41045
10.4%
51282
12.8%
61467
14.7%
71651
16.5%
81398
14.0%
9507
 
5.1%
ValueCountFrequency (%)
105
 
< 0.1%
9507
 
5.1%
81398
14.0%
71651
16.5%
61467
14.7%
51282
12.8%
41045
10.4%
3946
9.5%
2919
9.2%
1749
7.5%

SMRack
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.6 KiB
0
9183 
1
 
817
817
 
1

Length

Max length3
Median length1
Mean length1.00019998
Min length1

Characters and Unicode

Total characters10003
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09183
91.8%
1817
 
8.2%
8171
 
< 0.1%
2021-02-23T12:07:47.767308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T12:07:47.860439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09183
91.8%
1817
 
8.2%
8171
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09183
91.8%
1818
 
8.2%
81
 
< 0.1%
71
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10003
100.0%

Most frequent character per category

ValueCountFrequency (%)
09183
91.8%
1818
 
8.2%
81
 
< 0.1%
71
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10003
100.0%

Most frequent character per script

ValueCountFrequency (%)
09183
91.8%
1818
 
8.2%
81
 
< 0.1%
71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10003
100.0%

Most frequent character per block

ValueCountFrequency (%)
09183
91.8%
1818
 
8.2%
81
 
< 0.1%
71
 
< 0.1%

LGRack
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.6 KiB
0
9304 
1
 
696
696
 
1

Length

Max length3
Median length1
Mean length1.00019998
Min length1

Characters and Unicode

Total characters10003
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09304
93.0%
1696
 
7.0%
6961
 
< 0.1%
2021-02-23T12:07:48.084076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T12:07:48.169812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09304
93.0%
1696
 
7.0%
6961
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09304
93.0%
1696
 
7.0%
62
 
< 0.1%
91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10003
100.0%

Most frequent character per category

ValueCountFrequency (%)
09304
93.0%
1696
 
7.0%
62
 
< 0.1%
91
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10003
100.0%

Most frequent character per script

ValueCountFrequency (%)
09304
93.0%
1696
 
7.0%
62
 
< 0.1%
91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10003
100.0%

Most frequent character per block

ValueCountFrequency (%)
09304
93.0%
1696
 
7.0%
62
 
< 0.1%
91
 
< 0.1%

Humid
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.6 KiB
0
9183 
1
 
817
817
 
1

Length

Max length3
Median length1
Mean length1.00019998
Min length1

Characters and Unicode

Total characters10003
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09183
91.8%
1817
 
8.2%
8171
 
< 0.1%
2021-02-23T12:07:48.402853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T12:07:48.485097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09183
91.8%
1817
 
8.2%
8171
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09183
91.8%
1818
 
8.2%
81
 
< 0.1%
71
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10003
100.0%

Most frequent character per category

ValueCountFrequency (%)
09183
91.8%
1818
 
8.2%
81
 
< 0.1%
71
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10003
100.0%

Most frequent character per script

ValueCountFrequency (%)
09183
91.8%
1818
 
8.2%
81
 
< 0.1%
71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10003
100.0%

Most frequent character per block

ValueCountFrequency (%)
09183
91.8%
1818
 
8.2%
81
 
< 0.1%
71
 
< 0.1%

Spcork
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.6 KiB
0
9318 
1
 
682
682
 
1

Length

Max length3
Median length1
Mean length1.00019998
Min length1

Characters and Unicode

Total characters10003
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09318
93.2%
1682
 
6.8%
6821
 
< 0.1%
2021-02-23T12:07:48.709457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T12:07:48.791626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09318
93.2%
1682
 
6.8%
6821
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09318
93.2%
1682
 
6.8%
61
 
< 0.1%
81
 
< 0.1%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10003
100.0%

Most frequent character per category

ValueCountFrequency (%)
09318
93.2%
1682
 
6.8%
61
 
< 0.1%
81
 
< 0.1%
21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10003
100.0%

Most frequent character per script

ValueCountFrequency (%)
09318
93.2%
1682
 
6.8%
61
 
< 0.1%
81
 
< 0.1%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10003
100.0%

Most frequent character per block

ValueCountFrequency (%)
09318
93.2%
1682
 
6.8%
61
 
< 0.1%
81
 
< 0.1%
21
 
< 0.1%

Bucket
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.6 KiB
0
9870 
1
 
130
130
 
1

Length

Max length3
Median length1
Mean length1.00019998
Min length1

Characters and Unicode

Total characters10003
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09870
98.7%
1130
 
1.3%
1301
 
< 0.1%
2021-02-23T12:07:49.009036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T12:07:49.093645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09870
98.7%
1130
 
1.3%
1301
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09871
98.7%
1131
 
1.3%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10003
100.0%

Most frequent character per category

ValueCountFrequency (%)
09871
98.7%
1131
 
1.3%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10003
100.0%

Most frequent character per script

ValueCountFrequency (%)
09871
98.7%
1131
 
1.3%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10003
100.0%

Most frequent character per block

ValueCountFrequency (%)
09871
98.7%
1131
 
1.3%
31
 
< 0.1%

Access
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.6 KiB
0
8009 
1
1579 
2
 
355
3
 
57
2460
 
1

Length

Max length4
Median length1
Mean length1.00029997
Min length1

Characters and Unicode

Total characters10004
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
08009
80.1%
11579
 
15.8%
2355
 
3.5%
357
 
0.6%
24601
 
< 0.1%
2021-02-23T12:07:49.320610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T12:07:49.395569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
08009
80.1%
11579
 
15.8%
2355
 
3.5%
357
 
0.6%
24601
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08010
80.1%
11579
 
15.8%
2356
 
3.6%
357
 
0.6%
41
 
< 0.1%
61
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10004
100.0%

Most frequent character per category

ValueCountFrequency (%)
08010
80.1%
11579
 
15.8%
2356
 
3.6%
357
 
0.6%
41
 
< 0.1%
61
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10004
100.0%

Most frequent character per script

ValueCountFrequency (%)
08010
80.1%
11579
 
15.8%
2356
 
3.6%
357
 
0.6%
41
 
< 0.1%
61
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10004
100.0%

Most frequent character per block

ValueCountFrequency (%)
08010
80.1%
11579
 
15.8%
2356
 
3.6%
357
 
0.6%
41
 
< 0.1%
61
 
< 0.1%

Complain
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.6 KiB
0
9888 
1
 
112
112
 
1

Length

Max length3
Median length1
Mean length1.00019998
Min length1

Characters and Unicode

Total characters10003
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
09888
98.9%
1112
 
1.1%
1121
 
< 0.1%
2021-02-23T12:07:49.647406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T12:07:49.727557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09888
98.9%
1112
 
1.1%
1121
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09888
98.9%
1114
 
1.1%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10003
100.0%

Most frequent character per category

ValueCountFrequency (%)
09888
98.9%
1114
 
1.1%
21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10003
100.0%

Most frequent character per script

ValueCountFrequency (%)
09888
98.9%
1114
 
1.1%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10003
100.0%

Most frequent character per block

ValueCountFrequency (%)
09888
98.9%
1114
 
1.1%
21
 
< 0.1%

Mailfriend
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.6 KiB
0
8981 
1
1019 
1019
 
1

Length

Max length4
Median length1
Mean length1.00029997
Min length1

Characters and Unicode

Total characters10004
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
08981
89.8%
11019
 
10.2%
10191
 
< 0.1%
2021-02-23T12:07:49.929726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T12:07:50.004946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
08981
89.8%
11019
 
10.2%
10191
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08982
89.8%
11021
 
10.2%
91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10004
100.0%

Most frequent character per category

ValueCountFrequency (%)
08982
89.8%
11021
 
10.2%
91
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10004
100.0%

Most frequent character per script

ValueCountFrequency (%)
08982
89.8%
11021
 
10.2%
91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10004
100.0%

Most frequent character per block

ValueCountFrequency (%)
08982
89.8%
11021
 
10.2%
91
 
< 0.1%

Emailfriend
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.6 KiB
0
9489 
1
 
511
511
 
1

Length

Max length3
Median length1
Mean length1.00019998
Min length1

Characters and Unicode

Total characters10003
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
09489
94.9%
1511
 
5.1%
5111
 
< 0.1%
2021-02-23T12:07:50.207238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T12:07:50.287479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
09489
94.9%
1511
 
5.1%
5111
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
09489
94.9%
1513
 
5.1%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10003
100.0%

Most frequent character per category

ValueCountFrequency (%)
09489
94.9%
1513
 
5.1%
51
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common10003
100.0%

Most frequent character per script

ValueCountFrequency (%)
09489
94.9%
1513
 
5.1%
51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10003
100.0%

Most frequent character per block

ValueCountFrequency (%)
09489
94.9%
1513
 
5.1%
51
 
< 0.1%

Rand
Real number (ℝ≥0)

Distinct10000
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.5021574211
Minimum0.0002663478859
Maximum0.9997207494
Zeros0
Zeros (%)0.0%
Memory size78.3 KiB
2021-02-23T12:07:50.385549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0002663478859
5-th percentile0.05005658068
Q10.2495372499
median0.5059135857
Q30.7520553178
95-th percentile0.9499048046
Maximum0.9997207494
Range0.9994544015
Interquartile range (IQR)0.5025180679

Descriptive statistics

Standard deviation0.2900675656
Coefficient of variation (CV)0.5776426942
Kurtosis-1.206560432
Mean0.5021574211
Median Absolute Deviation (MAD)0.251060804
Skewness-0.0153711596
Sum5021.574211
Variance0.08413919264
MonotocityNot monotonic
2021-02-23T12:07:50.511226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.79060055871
 
< 0.1%
0.48633967231
 
< 0.1%
0.082048057041
 
< 0.1%
0.52262023451
 
< 0.1%
0.0064539556751
 
< 0.1%
0.33132185911
 
< 0.1%
0.76150325961
 
< 0.1%
0.57458866261
 
< 0.1%
0.44982336551
 
< 0.1%
0.48274740731
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
0.00026634788591
< 0.1%
0.00031156487021
< 0.1%
0.00050144850861
< 0.1%
0.00069739859511
< 0.1%
0.00073569842811
< 0.1%
0.00084743153381
< 0.1%
0.0008573953611
< 0.1%
0.00091367398781
< 0.1%
0.00094292123941
< 0.1%
0.00097595053061
< 0.1%
ValueCountFrequency (%)
0.99972074941
< 0.1%
0.99970938831
< 0.1%
0.99968524131
< 0.1%
0.99967533321
< 0.1%
0.99957756651
< 0.1%
0.99956485651
< 0.1%
0.99946696781
< 0.1%
0.99930833161
< 0.1%
0.99896222261
< 0.1%
0.99887692751
< 0.1%

Interactions

2021-02-23T12:06:52.136926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:52.288651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:52.419899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:52.560970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:52.687146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:52.806047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:52.920556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:53.039559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:53.164006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:53.279643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:53.393986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:53.513626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:53.635798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:53.753918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:53.886715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:54.009175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:54.146842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:54.262678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:54.379654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:54.517629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:54.654949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:54.794354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:54.927802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:55.052038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:55.179047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:55.310212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:55.446427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:55.572814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:55.698727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:55.830415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:55.963935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:56.090287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:56.224883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:56.348135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:56.470129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:56.595022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:56.721903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:56.853877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:56.985077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:57.121438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:57.251861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:57.375024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:57.498899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:57.626071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:57.763190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:57.889828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:58.014180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:58.144255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:58.272792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:58.398979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:58.528884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:58.649916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:58.769851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:58.893898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:59.016329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:59.145897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:59.286129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:59.421453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:59.562573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:59.694114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:59.827037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:06:59.964364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:00.112021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:00.247161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:00.376559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:00.534216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-23T12:07:29.283784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:29.414992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:29.563325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:29.731374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:29.867684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:29.988714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:30.108721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:30.263879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:30.400220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:30.553671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:30.714709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:30.859994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:31.011132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:31.146708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:31.283811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:31.413115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:31.540065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:31.665041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:31.794834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:31.912739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:32.026636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:32.142836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:32.250409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:32.366039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:32.482180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:32.608008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:32.727695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:32.837797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:32.950872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:33.068841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:33.192096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:33.304381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:33.415602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:33.534173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:33.686311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:33.802683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:33.925393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:34.034919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:34.146763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:34.263460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:34.373762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:34.493549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:34.612951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:34.745585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:35.241332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:35.356044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:35.471575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:35.592986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:35.719598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:35.834586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:35.947857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:36.069471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:36.189937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:36.306973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:36.432403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:36.548399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:36.666003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:36.788977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:36.905541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:37.025721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:37.141846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:37.267675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:37.388073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:37.503989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:37.618047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:37.738400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:37.867179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:37.984424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:38.101111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:38.225160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:38.350952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:38.473431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:38.611719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:38.738497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T12:07:38.862017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-23T12:07:50.652905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-23T12:07:51.030458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-23T12:07:51.400284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-23T12:07:51.786812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-23T12:07:52.112424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-23T12:07:39.119131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-23T12:07:40.745613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-23T12:07:41.196136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-23T12:07:41.386243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CustidDayswusAgeEduIncomeKidhomeTeenhomeFreqRecencyMonetaryLTVPerdealDryredSweetredDrywhSweetwhDessertExoticWebPurchaseWebVisitSMRackLGRackHumidSpcorkBucketAccessComplainMailfriendEmailfriendRand
05325.0653.055.020.078473.00.00.02018.0826.0445.07.067.04.026.02.01.01.036.050000000000.240092
13956.01041.075.018.0105087.00.00.03633.01852.0539.02.049.00.046.01.03.00.020.040001000000.435944
23681.0666.018.012.027984.01.00.0456.039.0-7.088.04.029.014.032.021.048.060.080000000000.350584
32829.01049.042.016.061748.01.01.0246.037.0-6.070.086.01.011.01.01.055.059.070000000110.594082
48788.0837.047.016.065789.00.01.023.036.04.035.085.00.012.02.01.028.063.060000000000.782248
54356.0916.054.020.076751.00.00.01717.0658.0185.04.062.07.027.04.00.030.036.050000000100.726999
67003.0874.042.015.051644.01.01.0219.033.0-5.074.075.01.022.00.02.023.044.051000010100.464131
71815.01063.070.016.083942.00.00.02920.01407.0440.04.040.03.045.03.09.02.013.020000000000.022904
89139.0853.026.017.033186.01.01.0286.021.0-6.089.023.016.040.011.011.030.066.080000000000.333297
96511.0881.053.017.088538.00.01.01712.0651.0195.016.088.02.09.01.01.011.039.050000000000.870078

Last rows

CustidDayswusAgeEduIncomeKidhomeTeenhomeFreqRecencyMonetaryLTVPerdealDryredSweetredDrywhSweetwhDessertExoticWebPurchaseWebVisitSMRackLGRackHumidSpcorkBucketAccessComplainMailfriendEmailfriendRand
99918810.0868.00048.000018.000072973.0000.00001.00001164.0000350.000033.000041.000091.00001.00005.00001.00002.000016.000038.000040000000000.118905
99929745.01174.00025.000018.000035468.0001.00000.0000338.000065.0000-3.000060.000062.00004.000030.00000.00004.00004.000068.000090000000000.935576
99938720.01097.00044.000016.000058191.0001.00001.0000665.0000152.0000-19.000070.000052.00004.000034.00005.00005.00002.000046.000070000000000.524598
99947989.0774.00043.000018.000042853.0001.00001.0000433.000059.0000-17.000090.000073.00001.000025.00001.00000.00007.000067.000080000000000.762488
99951383.01132.00057.000020.000081033.0000.00001.00001959.0000776.0000187.000022.000078.00000.000020.00001.00001.000011.000027.000041000010100.820415
99964070.0596.00066.000015.000084714.0000.00000.00001845.0000720.0000391.00005.000030.000012.000036.000010.000012.000013.000018.000020000000000.905248
99977909.0619.00018.000012.000040466.0000.00000.0000365.000047.00005.000023.00006.000024.000010.000038.000022.000041.000058.000050000000000.679388
99984158.01107.00033.000016.000053661.0001.00000.00001368.000015.00002.000035.000018.000013.000045.000011.000013.000013.000060.000060000000000.016766
99994914.0979.00055.000016.000094926.0000.00001.00002528.00001148.0000293.00007.000063.000010.000013.000011.00003.00004.000034.000050000000000.362494
10000NaN898.10247.927316.739169904.3580.41880.46981462.4068622.5552209.071232.397250.38277.054528.52137.06986.947416.546642.3762581769681768213024601121019511NaN